Research on Urban Sustainability Based on Neural Network Models and GIS Methods

Chunxia Zhang (), Shuo Yu and Junxue Zhang
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Chunxia Zhang: School of Fine Art and Design, Yangzhou University, Yangzhou 225009, China
Shuo Yu: School of Fine Art and Design, Yangzhou University, Yangzhou 225009, China
Junxue Zhang: School of Civil Engineering and Architecture, Jiangsu University of Science and Technology, Zhenjiang 212100, China

Sustainability, 2025, vol. 17, issue 2, 1-35

Abstract: Ecologically sustainable urban design plays a pivotal role in mitigating climate change. This study develops an indicator group consisting of urban ecological emergy, land use change, population density, ecological services, habitat quality, enhanced vegetation index, carbon emissions, and carbon storage to assess urban sustainability. By leveraging a dataset from 2000 to 2020, we employ a neural network to predict emergy sustainability indicators over a time series, projecting the sustainable status of Xuzhou City from 2020 to 2050. The findings indicate that urbanization has led to significant changes in land use, population distribution, ecological service patterns, habitat quality degradation, vegetation fragmentation, and fluctuating carbon dynamics. Cropland constitutes the predominant land type (90.6%), followed by built-up land (8.49%). The neural network predictions suggest that Xuzhou City’s sustainable status is subject to volatility (15–20%), with stability expected only as the city matures into a developed urban area. This research introduces a novel approach to urban sustainability analysis and provides insights for policy development aimed at fostering sustainable urban growth.

Keywords: urban sustainability; neural network; GIS analysis; emergy calculation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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